TensorFlow Lite image classification Android example application
This is an example application for TensorFlow Lite on Android. It uses Image classification to continuously classify whatever it sees from the device's back camera. Inference is performed using the TensorFlow Lite Java API. The demo app classifies frames in real-time, displaying the top most probable classifications. It allows the user to choose between a floating point or quantized model, select the thread count, and decide whether to run on CPU, GPU, or via NNAPI.
These instructions walk you through building and running the demo on an Android device. For an explanation of the source, see TensorFlow Lite Android image classification example.
We provide 4 models bundled in this App: MobileNetV1 (float), MobileNetV1 (quantized), EfficientNetLite (float) and EfficientNetLite (quantized). Particularly, we chose "mobilenet_v1_1.0_224" and "efficientnet-lite0". MobileNets are classical models, while EfficientNets are the latest work. The chosen EfficientNet (lite0) has comparable speed with MobileNetV1, and on the ImageNet dataset, EfficientNet-lite0 out performs MobileNetV1 by ~4% in terms of top-1 accuracy.
For details of the model used, visit Image classification.
Downloading, extracting, and placing the model in the assets folder is managed automatically by download.gradle.
Android Studio 3.2 (installed on a Linux, Mac or Windows machine)
Android device in developer mode with USB debugging enabled
USB cable (to connect Android device to your computer)
Build and run
Step 1. Clone the TensorFlow examples source code
Clone the TensorFlow examples GitHub repository to your computer to get the demo application.
git clone https://github.com/tensorflow/examples
Open the TensorFlow source code in Android Studio. To do this, open Android
Studio and select
Open an existing project, setting the folder to
Step 2. Build the Android Studio project
Build -> Make Project and check that the project builds successfully.
You will need Android SDK configured in the settings. You'll need at least SDK
version 23. The
build.gradle file will prompt you to download any missing
Switch between inference solutions (Task library vs Support Library)
This Image Classification Android reference app demonstrates two implementation solutions:
app folder shows how to change
flavorDimensions "tfliteInference" to switch between the two solutions.
Inside Android Studio, you can change the build variant to whichever one you
want to build and run—just go to
Build > Select Build Variant and select one
from the drop-down menu. See
configure product flavors in Android Studio
for more details.
For gradle CLI, running
./gradlew build can create APKs under
app/build/outputs/apk for both solutions.
Note: If you simply want the out-of-box API to run the app, we recommend
lib_task_api for inference. If you want to customize your own models and
control the detail of inputs and outputs, it might be easier to adapt your model
inputs and outputs by using
download.gradle directs gradle to download the two models used in the
example, placing them into
`build.gradle` is configured to use TensorFlow Lite's nightly build.
If you see a build error related to compatibility with Tensorflow Lite's Java API (for example, `method X is undefined for type Interpreter`), there has likely been a backwards compatible change to the API. You will need to run `git pull` in the examples repo to obtain a version that is compatible with the nightly build.
Step 3. Install and run the app
Connect the Android device to the computer and be sure to approve any ADB
permission prompts that appear on your phone. Select
Run -> Run app. Select
the deployment target in the connected devices to the device on which the app
will be installed. This will install the app on the device.
To test the app, open the app called
TFL Classify on your device. When you run
the app the first time, the app will request permission to access the camera.
Re-installing the app may require you to uninstall the previous installations.
Do not delete the assets folder content. If you explicitly deleted the files,
Build -> Rebuild to re-download the deleted model files into the assets